The Characteristics of Cognitive Impairment and Their Effects on Functional Outcome After Inpatient Rehabilitation in Subacute Stroke Patients
Bibliographic record
Abstract
Objective To determine the frequency and characteristics of vascular cognitive impairment (VCI) in patients with subacute stroke who underwent inpatient rehabilitation and to analyze whether cognitive function can predict functional assessments after rehabilitation.Methods We retrospectively reviewed the medical records of patients who were admitted to our rehabilitation center after experiencing a stroke between October 2014 and September 2015.We analyzed the data from 104 patients who completed neuropsychological assessments within 3 months after onset of a stroke.Results Cognitive impairment was present in 86 out of 104 patients (82.6%).The most common impairment was in visuospatial function (65, 62.5%) followed by executive function (63, 60.5%), memory (62, 59.6%), and language function (34, 32.6%).Patients with impairment in the visuospatial and executive domains had poor scores of functional assessments at both admission and discharge (p<0.05).A multivariate analysis revealed that age (b= -0.173) and the scores on the modified Rankin Scale (b=-0.178),Korean version of the Modified Barthel Index (K-MBI) (b=0.489) at admission, and Trail-Making Test A (TMT-A) (b=0.228) were related to the final K-MBI score at discharge (adjusted R 2 =0.646).Conclusion In our study, VCI was highly prevalent in patients with stroke.TMT-A scores were highly predictive of their final K-MBI score.Collectively, our results suggest that post-stroke executive dysfunction is a significant and independent predictor of functional outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".